|
--- |
|
language: |
|
- "ja" |
|
tags: |
|
- "japanese" |
|
- "masked-lm" |
|
- "wikipedia" |
|
license: "cc-by-sa-4.0" |
|
pipeline_tag: "fill-mask" |
|
mask_token: "[MASK]" |
|
widget: |
|
- text: "日本に着いたら[MASK]を訪ねなさい。" |
|
--- |
|
|
|
# deberta-base-japanese-wikipedia |
|
|
|
## Model Description |
|
|
|
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 109 hours 27 minutes for training. You can fine-tune `deberta-base-japanese-wikipedia` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia-ud-head), and so on. |
|
|
|
## How to Use |
|
|
|
```py |
|
from transformers import AutoTokenizer,AutoModelForMaskedLM |
|
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") |
|
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") |
|
``` |
|
|
|
## Reference |
|
|
|
安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43. |
|
|
|
|
